ML Anthology

Unsupervised Segmentation of Objects Using Efficient Learning

Himanshu Arora, Nicolas Loeff, David A. Forsyth, Narendra Ahuja
CVPR 2007
doi:10.1109/CVPR.2007.383011 /cvpr/2007/arora2007cvpr-unsupervised/

Abstract

We describe an unsupervised method to segment objects detected in images using a novel variant of an interest point template, which is very efficient to train and evaluate. Once an object has been detected, our method segments an image using a conditional random field (CRF) model. This model integrates image gradients, the location and scale of the object, the presence of object parts, and the tendency of these parts to have characteristic patterns of edges nearby. We enhance our method using multiple unsegmented images of objects to learn the parameters of the CRF, in an iterative conditional maximization framework. We show quantitative results on images of real scenes that demonstrate the accuracy of segmentation.

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Cite

Text

Arora et al. "Unsupervised Segmentation of Objects Using Efficient Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383011

Markdown

[Arora et al. "Unsupervised Segmentation of Objects Using Efficient Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/arora2007cvpr-unsupervised/) doi:10.1109/CVPR.2007.383011

BibTeX

@inproceedings{arora2007cvpr-unsupervised,
  title     = {{Unsupervised Segmentation of Objects Using Efficient Learning}},
  author    = {Arora, Himanshu and Loeff, Nicolas and Forsyth, David A. and Ahuja, Narendra},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383011},
  url       = {https://mlanthology.org/cvpr/2007/arora2007cvpr-unsupervised/}
}